Refactor object detection box predictors and fix some issues with model_main.

research/object_detection/README.md

@@ -79,7 +79,7 @@ Extras:
       Run the evaluation for the Open Images Challenge 2018</a><br>
   * <a href='g3doc/tpu_compatibility.md'>
       TPU compatible detection pipelines</a><br>
-  *  <a href='g3doc/running_on_mobile_tensorflowlite.md'>
+  * <a href='g3doc/running_on_mobile_tensorflowlite.md'>
       Running object detection on mobile devices with TensorFlow Lite</a><br>
 
 ## Getting Help

research/object_detection/anchor_generators/multiple_grid_anchor_generator.py

@@ -157,12 +157,10 @@ def _generate(self, feature_map_shape_list, im_height=1, im_width=1):
         correspond to an 8x8 layer followed by a 7x7 layer.
       im_height: the height of the image to generate the grid for. If both
         im_height and im_width are 1, the generated anchors default to
-        normalized coordinates, otherwise absolute coordinates are used for the
-        grid.
+        absolute coordinates, otherwise normalized coordinates are produced.
       im_width: the width of the image to generate the grid for. If both
         im_height and im_width are 1, the generated anchors default to
-        normalized coordinates, otherwise absolute coordinates are used for the
-        grid.
+        absolute coordinates, otherwise normalized coordinates are produced.
 
     Returns:
       boxes_list: a list of BoxLists each holding anchor boxes corresponding to

research/object_detection/anchor_generators/multiscale_grid_anchor_generator.py

@@ -57,14 +57,12 @@ def __init__(self, min_level, max_level, anchor_scale, aspect_ratios,
     self._scales_per_octave = scales_per_octave
     self._normalize_coordinates = normalize_coordinates
 
+    scales = [2**(float(scale) / scales_per_octave)
+              for scale in xrange(scales_per_octave)]
+    aspects = list(aspect_ratios)
+
     for level in range(min_level, max_level + 1):
       anchor_stride = [2**level, 2**level]
-      scales = []
-      aspects = []
-      for scale in range(scales_per_octave):
-        scales.append(2**(float(scale) / scales_per_octave))
-      for aspect_ratio in aspect_ratios:
-        aspects.append(aspect_ratio)
       base_anchor_size = [2**level * anchor_scale, 2**level * anchor_scale]
       self._anchor_grid_info.append({
           'level': level,
@@ -84,7 +82,7 @@ def num_anchors_per_location(self):
     return len(self._anchor_grid_info) * [
         len(self._aspect_ratios) * self._scales_per_octave]
 
-  def _generate(self, feature_map_shape_list, im_height, im_width):
+  def _generate(self, feature_map_shape_list, im_height=1, im_width=1):
     """Generates a collection of bounding boxes to be used as anchors.
 
     Currently we require the input image shape to be statically defined.  That
@@ -95,14 +93,20 @@ def _generate(self, feature_map_shape_list, im_height, im_width):
         format [(height_0, width_0), (height_1, width_1), ...]. For example,
         setting feature_map_shape_list=[(8, 8), (7, 7)] asks for anchors that
         correspond to an 8x8 layer followed by a 7x7 layer.
-      im_height: the height of the image to generate the grid for.
-      im_width: the width of the image to generate the grid for.
+      im_height: the height of the image to generate the grid for. If both
+        im_height and im_width are 1, anchors can only be generated in
+        absolute coordinates.
+      im_width: the width of the image to generate the grid for. If both
+        im_height and im_width are 1, anchors can only be generated in
+        absolute coordinates.
 
     Returns:
       boxes_list: a list of BoxLists each holding anchor boxes corresponding to
         the input feature map shapes.
     Raises:
       ValueError: if im_height and im_width are not integers.
+      ValueError: if im_height and im_width are 1, but normalized coordinates
+        were requested.
     """
     if not isinstance(im_height, int) or not isinstance(im_width, int):
       raise ValueError('MultiscaleGridAnchorGenerator currently requires '
@@ -118,9 +122,9 @@ def _generate(self, feature_map_shape_list, im_height, im_width):
       feat_h = feat_shape[0]
       feat_w = feat_shape[1]
       anchor_offset = [0, 0]
-      if im_height % 2.0**level == 0:
+      if im_height % 2.0**level == 0 or im_height == 1:
         anchor_offset[0] = stride / 2.0
-      if im_width % 2.0**level == 0:
+      if im_width % 2.0**level == 0 or im_width == 1:
         anchor_offset[1] = stride / 2.0
       ag = grid_anchor_generator.GridAnchorGenerator(
           scales,
@@ -131,6 +135,11 @@ def _generate(self, feature_map_shape_list, im_height, im_width):
       (anchor_grid,) = ag.generate(feature_map_shape_list=[(feat_h, feat_w)])
 
       if self._normalize_coordinates:
+        if im_height == 1 or im_width == 1:
+          raise ValueError(
+              'Normalized coordinates were requested upon construction of the '
+              'MultiscaleGridAnchorGenerator, but a subsequent call to '
+              'generate did not supply dimension information.')
         anchor_grid = box_list_ops.to_normalized_coordinates(
             anchor_grid, im_height, im_width, check_range=False)
       anchor_grid_list.append(anchor_grid)

research/object_detection/anchor_generators/multiscale_grid_anchor_generator_test.py

@@ -47,6 +47,40 @@ def test_construct_single_anchor(self):
       anchor_corners_out = anchor_corners.eval()
       self.assertAllClose(anchor_corners_out, exp_anchor_corners)
 
+  def test_construct_single_anchor_unit_dimensions(self):
+    min_level = 5
+    max_level = 5
+    anchor_scale = 1.0
+    aspect_ratios = [1.0]
+    scales_per_octave = 1
+    im_height = 1
+    im_width = 1
+    feature_map_shape_list = [(2, 2)]
+    # Positive offsets are produced.
+    exp_anchor_corners = [[0, 0, 32, 32],
+                          [0, 32, 32, 64],
+                          [32, 0, 64, 32],
+                          [32, 32, 64, 64]]
+
+    anchor_generator = mg.MultiscaleGridAnchorGenerator(
+        min_level, max_level, anchor_scale, aspect_ratios, scales_per_octave,
+        normalize_coordinates=False)
+    anchors_list = anchor_generator.generate(
+        feature_map_shape_list, im_height=im_height, im_width=im_width)
+    anchor_corners = anchors_list[0].get()
+
+    with self.test_session():
+      anchor_corners_out = anchor_corners.eval()
+      self.assertAllClose(anchor_corners_out, exp_anchor_corners)
+
+  def test_construct_normalized_anchors_fails_with_unit_dimensions(self):
+    anchor_generator = mg.MultiscaleGridAnchorGenerator(
+        min_level=5, max_level=5, anchor_scale=1.0, aspect_ratios=[1.0],
+        scales_per_octave=1, normalize_coordinates=True)
+    with self.assertRaisesRegexp(ValueError, 'Normalized coordinates'):
+      anchor_generator.generate(
+          feature_map_shape_list=[(2, 2)], im_height=1, im_width=1)
+
   def test_construct_single_anchor_in_normalized_coordinates(self):
     min_level = 5
     max_level = 5
@@ -94,7 +128,7 @@ def test_construct_single_anchor_fails_with_tensor_image_size(self):
     anchor_generator = mg.MultiscaleGridAnchorGenerator(
         min_level, max_level, anchor_scale, aspect_ratios, scales_per_octave,
         normalize_coordinates=False)
-    with self.assertRaises(ValueError):
+    with self.assertRaisesRegexp(ValueError, 'statically defined'):
       anchor_generator.generate(
           feature_map_shape_list, im_height=im_height, im_width=im_width)
 

research/object_detection/builders/box_predictor_builder.py

@@ -15,7 +15,12 @@
 
 """Function to build box predictor from configuration."""
 
-from object_detection.core import box_predictor
+from object_detection.predictors import convolutional_box_predictor
+from object_detection.predictors import mask_rcnn_box_predictor
+from object_detection.predictors import rfcn_box_predictor
+from object_detection.predictors.mask_rcnn_heads import box_head
+from object_detection.predictors.mask_rcnn_heads import class_head
+from object_detection.predictors.mask_rcnn_heads import mask_head
 from object_detection.protos import box_predictor_pb2
 
 
@@ -48,92 +53,112 @@ def build(argscope_fn, box_predictor_config, is_training, num_classes):
   box_predictor_oneof = box_predictor_config.WhichOneof('box_predictor_oneof')
 
   if  box_predictor_oneof == 'convolutional_box_predictor':
-    conv_box_predictor = box_predictor_config.convolutional_box_predictor
-    conv_hyperparams_fn = argscope_fn(conv_box_predictor.conv_hyperparams,
+    config_box_predictor = box_predictor_config.convolutional_box_predictor
+    conv_hyperparams_fn = argscope_fn(config_box_predictor.conv_hyperparams,
                                       is_training)
-    box_predictor_object = box_predictor.ConvolutionalBoxPredictor(
-        is_training=is_training,
-        num_classes=num_classes,
-        conv_hyperparams_fn=conv_hyperparams_fn,
-        min_depth=conv_box_predictor.min_depth,
-        max_depth=conv_box_predictor.max_depth,
-        num_layers_before_predictor=(conv_box_predictor.
-                                     num_layers_before_predictor),
-        use_dropout=conv_box_predictor.use_dropout,
-        dropout_keep_prob=conv_box_predictor.dropout_keep_probability,
-        kernel_size=conv_box_predictor.kernel_size,
-        box_code_size=conv_box_predictor.box_code_size,
-        apply_sigmoid_to_scores=conv_box_predictor.apply_sigmoid_to_scores,
-        class_prediction_bias_init=(conv_box_predictor.
-                                    class_prediction_bias_init),
-        use_depthwise=conv_box_predictor.use_depthwise
-    )
+    box_predictor_object = (
+        convolutional_box_predictor.ConvolutionalBoxPredictor(
+            is_training=is_training,
+            num_classes=num_classes,
+            conv_hyperparams_fn=conv_hyperparams_fn,
+            min_depth=config_box_predictor.min_depth,
+            max_depth=config_box_predictor.max_depth,
+            num_layers_before_predictor=(
+                config_box_predictor.num_layers_before_predictor),
+            use_dropout=config_box_predictor.use_dropout,
+            dropout_keep_prob=config_box_predictor.dropout_keep_probability,
+            kernel_size=config_box_predictor.kernel_size,
+            box_code_size=config_box_predictor.box_code_size,
+            apply_sigmoid_to_scores=config_box_predictor.
+            apply_sigmoid_to_scores,
+            class_prediction_bias_init=(
+                config_box_predictor.class_prediction_bias_init),
+            use_depthwise=config_box_predictor.use_depthwise))
     return box_predictor_object
 
   if  box_predictor_oneof == 'weight_shared_convolutional_box_predictor':
-    conv_box_predictor = (box_predictor_config.
-                          weight_shared_convolutional_box_predictor)
-    conv_hyperparams_fn = argscope_fn(conv_box_predictor.conv_hyperparams,
+    config_box_predictor = (
+        box_predictor_config.weight_shared_convolutional_box_predictor)
+    conv_hyperparams_fn = argscope_fn(config_box_predictor.conv_hyperparams,
                                       is_training)
-    box_predictor_object = box_predictor.WeightSharedConvolutionalBoxPredictor(
-        is_training=is_training,
-        num_classes=num_classes,
-        conv_hyperparams_fn=conv_hyperparams_fn,
-        depth=conv_box_predictor.depth,
-        num_layers_before_predictor=(
-            conv_box_predictor.num_layers_before_predictor),
-        kernel_size=conv_box_predictor.kernel_size,
-        box_code_size=conv_box_predictor.box_code_size,
-        class_prediction_bias_init=conv_box_predictor.
-        class_prediction_bias_init,
-        use_dropout=conv_box_predictor.use_dropout,
-        dropout_keep_prob=conv_box_predictor.dropout_keep_probability,
-        share_prediction_tower=conv_box_predictor.share_prediction_tower)
+    apply_batch_norm = config_box_predictor.conv_hyperparams.HasField(
+        'batch_norm')
+    box_predictor_object = (
+        convolutional_box_predictor.WeightSharedConvolutionalBoxPredictor(
+            is_training=is_training,
+            num_classes=num_classes,
+            conv_hyperparams_fn=conv_hyperparams_fn,
+            depth=config_box_predictor.depth,
+            num_layers_before_predictor=(
+                config_box_predictor.num_layers_before_predictor),
+            kernel_size=config_box_predictor.kernel_size,
+            box_code_size=config_box_predictor.box_code_size,
+            class_prediction_bias_init=config_box_predictor.
+            class_prediction_bias_init,
+            use_dropout=config_box_predictor.use_dropout,
+            dropout_keep_prob=config_box_predictor.dropout_keep_probability,
+            share_prediction_tower=config_box_predictor.share_prediction_tower,
+            apply_batch_norm=apply_batch_norm))
     return box_predictor_object
 
   if box_predictor_oneof == 'mask_rcnn_box_predictor':
-    mask_rcnn_box_predictor = box_predictor_config.mask_rcnn_box_predictor
-    fc_hyperparams_fn = argscope_fn(mask_rcnn_box_predictor.fc_hyperparams,
+    config_box_predictor = box_predictor_config.mask_rcnn_box_predictor
+    fc_hyperparams_fn = argscope_fn(config_box_predictor.fc_hyperparams,
                                     is_training)
     conv_hyperparams_fn = None
-    if mask_rcnn_box_predictor.HasField('conv_hyperparams'):
+    if config_box_predictor.HasField('conv_hyperparams'):
       conv_hyperparams_fn = argscope_fn(
-          mask_rcnn_box_predictor.conv_hyperparams, is_training)
-    box_predictor_object = box_predictor.MaskRCNNBoxPredictor(
+          config_box_predictor.conv_hyperparams, is_training)
+    box_prediction_head = box_head.BoxHead(
         is_training=is_training,
         num_classes=num_classes,
         fc_hyperparams_fn=fc_hyperparams_fn,
-        use_dropout=mask_rcnn_box_predictor.use_dropout,
-        dropout_keep_prob=mask_rcnn_box_predictor.dropout_keep_probability,
-        box_code_size=mask_rcnn_box_predictor.box_code_size,
-        conv_hyperparams_fn=conv_hyperparams_fn,
-        predict_instance_masks=mask_rcnn_box_predictor.predict_instance_masks,
-        mask_height=mask_rcnn_box_predictor.mask_height,
-        mask_width=mask_rcnn_box_predictor.mask_width,
-        mask_prediction_num_conv_layers=(
-            mask_rcnn_box_predictor.mask_prediction_num_conv_layers),
-        mask_prediction_conv_depth=(
-            mask_rcnn_box_predictor.mask_prediction_conv_depth),
-        masks_are_class_agnostic=(
-            mask_rcnn_box_predictor.masks_are_class_agnostic),
-        predict_keypoints=mask_rcnn_box_predictor.predict_keypoints,
+        use_dropout=config_box_predictor.use_dropout,
+        dropout_keep_prob=config_box_predictor.dropout_keep_probability,
+        box_code_size=config_box_predictor.box_code_size,
         share_box_across_classes=(
-            mask_rcnn_box_predictor.share_box_across_classes))
+            config_box_predictor.share_box_across_classes))
+    class_prediction_head = class_head.ClassHead(
+        is_training=is_training,
+        num_classes=num_classes,
+        fc_hyperparams_fn=fc_hyperparams_fn,
+        use_dropout=config_box_predictor.use_dropout,
+        dropout_keep_prob=config_box_predictor.dropout_keep_probability)
+    third_stage_heads = {}
+    if config_box_predictor.predict_instance_masks:
+      third_stage_heads[
+          mask_rcnn_box_predictor.MASK_PREDICTIONS] = mask_head.MaskHead(
+              num_classes=num_classes,
+              conv_hyperparams_fn=conv_hyperparams_fn,
+              mask_height=config_box_predictor.mask_height,
+              mask_width=config_box_predictor.mask_width,
+              mask_prediction_num_conv_layers=(
+                  config_box_predictor.mask_prediction_num_conv_layers),
+              mask_prediction_conv_depth=(
+                  config_box_predictor.mask_prediction_conv_depth),
+              masks_are_class_agnostic=(
+                  config_box_predictor.masks_are_class_agnostic))
+    box_predictor_object = mask_rcnn_box_predictor.MaskRCNNBoxPredictor(
+        is_training=is_training,
+        num_classes=num_classes,
+        box_prediction_head=box_prediction_head,
+        class_prediction_head=class_prediction_head,
+        third_stage_heads=third_stage_heads)
     return box_predictor_object
 
   if box_predictor_oneof == 'rfcn_box_predictor':
-    rfcn_box_predictor = box_predictor_config.rfcn_box_predictor
-    conv_hyperparams_fn = argscope_fn(rfcn_box_predictor.conv_hyperparams,
+    config_box_predictor = box_predictor_config.rfcn_box_predictor
+    conv_hyperparams_fn = argscope_fn(config_box_predictor.conv_hyperparams,
                                       is_training)
-    box_predictor_object = box_predictor.RfcnBoxPredictor(
+    box_predictor_object = rfcn_box_predictor.RfcnBoxPredictor(
         is_training=is_training,
         num_classes=num_classes,
         conv_hyperparams_fn=conv_hyperparams_fn,
-        crop_size=[rfcn_box_predictor.crop_height,
-                   rfcn_box_predictor.crop_width],
-        num_spatial_bins=[rfcn_box_predictor.num_spatial_bins_height,
-                          rfcn_box_predictor.num_spatial_bins_width],
-        depth=rfcn_box_predictor.depth,
-        box_code_size=rfcn_box_predictor.box_code_size)
+        crop_size=[config_box_predictor.crop_height,
+                   config_box_predictor.crop_width],
+        num_spatial_bins=[config_box_predictor.num_spatial_bins_height,
+                          config_box_predictor.num_spatial_bins_width],
+        depth=config_box_predictor.depth,
+        box_code_size=config_box_predictor.box_code_size)
     return box_predictor_object
   raise ValueError('Unknown box predictor: {}'.format(box_predictor_oneof))